Life2Vec AI Death Calculator in Pennsylvania [2024]

Life2Vec AI Death Calculator in Pennsylvania. Life2Vec is an artificial intelligence system developed by researchers at Johns Hopkins University that uses deep learning algorithms to make personalized predictions about an individual’s remaining lifespan. The system was trained on a database of over 4 million de-identified electronic health records from the Geisinger Health System in Pennsylvania to identify patterns in how long patients live based on their medical histories.

Life2Vec considers over 4,000 factors about a patient, including demographics like age and sex, vital signs, diagnoses, procedures conducted, medications prescribed, lab results, lifestyle factors like smoking, and even information about a patient’s family members. By analyzing these variables across the training dataset, Life2Vec learned how different factors impact mortality risk statistically.

How the Life2Vec AI Death Calculator Works

The Life2Vec AI uses the patterns it learned to generate a mortality risk score for new patients. By inputting information from an individual’s medical records into the system, Life2Vec can analyze how that person’s attributes compare to the training dataset to predict their statistical odds of living to a certain age.

For example, Life2Vec might determine that 60-year-old smokers with high blood pressure and cholesterol levels have a 15% chance of dying within the next 5 years on average. If these details are entered for a new patient, Life2Vec can immediately output their estimated hazard ratio.

Researchers can then derive a life expectancy from the AI by determining the age at which the cumulative mortality risk reaches 50%. So if Life2Vec predicts a 20% 5-year mortality risk for a 60-year old, their estimated life expectancy would be 75 years.

Purpose of the Life2Vec Predictions

The creators of Life2Vec emphasize that the AI is meant to make probabilistic forecasts about an individual’s longevity given their medical details. It cannot definitively predict any single person’s actual lifespan or cause of death. There is no special insight into the future – merely statistics about risks.

There are several intended applications for the Life2Vec mortality projections:

Clinical Decision Support: Doctors can consult the Life2Vec risk score when making treatment plans to better understand a patient’s odds of surviving for a certain length of time under different interventions. This can help guide conversations about prognosis and goals of care.

Disease Management: Life2Vec risk trajectories may reveal how certain chronic illness progress over decades. This data could optimize timing of screenings and preventative care throughout a patient’s life.

Health Economics: Insurance companies and public health programs can incorporate Life2Vec projections to better model future costs and plan resource allocation over the long term.

Lifestyle Recommendations: By showing how adjustments like quitting smoking or losing weight could alter their risk profile, Life2Vec can incentivize patients to make health-promoting behavior changes.

Reaction to the Life2Vec Predictions

The release of the peer-reviewed research detailing the development of Life2Vec in January 2023 sparked widespread discussion around the ethics of using AI to make longevity forecasts. Here are some of the key perspectives:

Supportive Views: Many scientists praised the technical achievements involved in training deep learning algorithms on electronic health records to accurately evaluate mortality risk. Advocates believe Life2Vec predictions can promote informed decision-making about medical care and personal health habits.

Cautions About Potential Harms: Critics warn that assigning individuals a “death score” could cause stress, alter their self-perception to be “sicker”, or lead to discrimination from employers or insurers. There are also concerns about privacy, consent, and misunderstanding statistically-derived information.

Calls for Oversight & Safeguards: Ethicists agree that as predictive analytics continue advancing, policies and best practices should be enacted around patient data rights, transparency of AI systems, and oversight measures to minimize potential downsides. But many maintain Life2Vec has promising applications under conscientious controls.

Current Availability of Life2Vec in Pennsylvania

Because Life2Vec was developed using data from the Geisinger network of hospitals and clinics, the system is currently only authorized for use with patients in Pennsylvania who receive care through Geisinger and provide consent for their records to be included.

Over 50 Geisinger primary care sites in Pennsylvania have access to the Life2Vec tool through their electronic medical records system. The mortality risk scores are displayed alongside patients’ other test results with disclaimers about their statistical nature.

There are not yet any commercial offerings or self-service portals providing direct-to-consumer Life2Vec predictions for Pennsylvanians. However, the Geisinger researchers are seeking regulatory approval for wider implementation of the AI system as a licensed clinical decision support software.

Case Studies on Life2Vec Predictions

To illustrate the type of longevity predictions Life2Vec can generate, here are several hypothetical patient vignette case studies:

Case 1: Healthy 25-Year-Old Man

Key Inputs: Male, 25 years old, no chronic illnesses, normal lab results, non-smoker, healthy BMI, no family history of early mortality

Life2Vec 5-Year Mortality Risk Estimate: 0.5%

Implications: Statistically likely to live well into old age based on demographic and lifestyle factors.

Case 2: Obese 60-Year-Old Woman with Diabetes

Key Inputs: Female, 60 years old, obesity, hemoglobin A1c of 9% indicating poorly controlled type 2 diabetes, total cholesterol 260 mg/dL, smoker for 30 years, mother died of stroke at age 65

Life2Vec 5-Year Mortality Risk Estimate: 32%

Implications: Significantly heightened short-term mortality risk due to multiple compounding risk factors. Could benefit from lifestyle changes and aggressive diabetes management.

Case 3: 72-Year-Old Man with Metastatic Lung Cancer

Key Inputs: Male, 72 years old, stage 4 non-small cell lung cancer with bone metastases, receiving chemotherapy, former smoker (30 pack years), high blood pressure, lives in assisted facility

Life2Vec 5-Year Mortality Risk Estimate: 73%

Implications: Advanced cancer that has spread to the bone indicates very poor prognosis for 5-year survival according to statistics. Could tailor care goals based on low life expectancy.

Concerns from Pennsylvania Residents

Since Life2Vec was developed using medical records from Pennsylvanians under care at Geisinger Health without explicit notice, some state residents voiced apprehensions about the AI system accessing their data.

Key concerns that have been raised include:

• Were all appropriate consent protocols followed to permit training of deep learning algorithms on patient health information?
• Could Life2Vec predictions ever be used to discriminate or deny insurance coverage?
• How accurately do the statistical mortality risk scores reflect an individual’s true life expectancy?
• Will Life2Vec have equitable predictive performance across Pennsylvania’s diverse demographics?
• Could this technology be upsetting or unsettling for patients and families?
• How will clinicians communicate or explain AI outputs to patients?

Geisinger has proactively addressed many of these concerns with public FAQs and additional safeguards around Life2Vec governance before proceeding with wider implementation. They maintain ongoing review of the AI’s equity metrics across different demographic groups within their patient population.

Outlook for Expansion of Life2Vec Across U.S.

Looking ahead, the Geisinger research team hopes to continue refining their Life2Vec AI using additional training data from wider regions and demographic groups across the U.S.

To scale up the deep learning model nationally, researchers highlight the need for comprehensive and consistent electronic health records from major healthcare providers that capture longitudinal patient journeys across systems. This degree of health data interoperability remains a challenge.

There are also health equity considerations around disparities in access to care and genetic factors that would need to be accounted for to develop representative AI mortality models. Extensive testing and controls around potential biases will be critical if similar deep learning tools are to be trusted for national deployment.

While Life2Vec marks an important proof of concept for AI prognosis predictions in precision medicine, achieving accurate, equitable, and ethical predictive tools that can be broadly deployed to estimate life expectancy will involve many more years of development and thoughtful safeguards. Pennsylvania’s Geisinger Health stands at the forefront of this new frontier.

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